Landsat7 ETM+ SLC-OFF数据的修复及其在武汉东湖水质反演中的应用

自从2003年5月31日，从陆地卫星 7发回的ETM+图像数据就存在缺陷。这是由于增强专题制图仪的扫描线校正器发生故障引起的。这些称为SLC OF数据的图像有一些黑色的不存在任何数据的扫描行。丢失的数据约占全景数据的25％，使它们难以正常使用。但是，数据本身仍然保留了良好的辐射和几何性质，如加以妥善修复，仍可以在一些特殊领域中使用。首先介绍了如何使用自适应局部回归算法(ALR)恢复这些图像，然后使用修复后的图像反演武汉东湖的水质参数。结果表明：ALR算法可以对SLC OF图像进行较好的修复，而且利用修复后的图像和东湖的地面水质监测数据，通过多元逐步回归分析，可以建立很好的叶绿素a、透明度、总磷以及总氮等水质参数的经验遥感反演模型，模型的相关系数R²分别为0.86、0.75、0.73和0.71。反演得到的水质参数分布与实际情况符合。这些数据有许多优点，如空间分辨率高、存档数据非常丰富、可以从NASA的服务器免费下载等。在其他遥感数据不足或无法获得的情况下，这些数据经过适当的修复，可以作为补充或替代数据使用。〖     

塔里木河下游输水前后NDVI差异分析

运用统计方法分析塔里木河下游英苏、喀尔达依、阿拉干三断面输水前后归一化植被指数(NDVI)的差异大小并推测河畔植被对输水的响应程度。结果表明:随着输水进行,研究区NDVI与地下水埋深逐渐呈负相关趋势,NDVI增幅随地下水埋深减小逐渐增大;输水前后相同断面NDVI差异极显著,根据输水前后NDVI差值大小确定植被恢复速度为:英苏>喀尔达依>阿拉干;各断面NDVI在输水前没有差异,输水后断面间NDVI差异显著,根据差异大小确定恢复程度顺序为英苏>喀尔达依>阿拉干;以大西海子水库周边NDVI为参照,确定经7年8次生态输水英苏、喀尔达依、阿拉干三断面植被恢复度分别为20.2%、18.4%和8.4%。以上研究表明,研究区植被恢复仍然有限,坚持持续输水很有必要。     

基于ETM 遥感影像的城市热岛效应监测技术研究

针对传统城市热岛效应监测技术方法的不足,探讨了利用热红外遥感影像进行城市热岛效应监测的方法。研究过程中采用密度分割法对广州市热岛分布状况进行模拟实验。最后结合其他统计资料,借助GIS等辅助工具,对热岛成因进行分析,提出缓解城市热岛效应的有效措施。实验结果表明,该种方法具有简便、准确、实用等特点,适合在我国城市开展进行热岛的常规监测。     

Measurement and Management of Human-Induced Patterns of Forest Fragmentation: A Case Study

In many tropical developing countries, the twin pressures of population and poverty are resulting in substantial fragmentation of forests, increasing the probability of extinction for many species, Forest fragmentation occurs when large contiguous forests are perforated by small holes or broken up into edges and smaller patches to form a nonforested matrix of open spaces. Thus, forest fragmentation refers not only to the area of forest cleared, but also to the pattern of this clearance, the resulting forest’s spatial properties. Both characteristics are important for species survivability. Apart from opening up forests to many abiotic and biotic influences, fragmentation can affect species dispersal and migration through its effects on forest connectivity. Landscape ecology conceptualizes connectivity as a gradient of critical thresholds, ranging from the large intact forest to the small unconnected forest patch. This article reports results from a multiple-scale analysis of forest fragmentation in Jamaica’s Cockpit Country, an area of once contiguous forest now under threat from human encroachment. Spatial forest data derived from classification of ETM+ satellite imagery are used to measure fragmentation patterns representing various degrees of forest connectivity and density. The results suggest that, overall, 81% of the region is in forest. However, fragmentation patterns also suggest that this forest is riven with extensive perforations indicative of an early stage in the decline of contiguity. The results provided by the spatial fragmentation model are a first step in the design of effective conservation and rehabilitation plans for the area. The article concludes with a discussion of possible multiscale management options for the region.     

上海崇明县植被覆盖度动态变化遥感监测研究

植被覆盖度是用于描述和评价生态系统的重要参数。基于1989、2001、2006年的TM/ETM+遥感数据,以上海市崇明县为研究区域,利用改进的像元二分模型,估算出不同时期内崇明县的植被覆盖度,并对其变化进行了定量分析。结果表明:1989-2001年,植被覆盖度负向变化地表约10.17%,正向变化约20.58%,乡镇和湿地区域正向变化的面积大于负向变化,农场区域则相反;2001-2006年,负向变化约29.1%,正向变化约12%,农场和湿地区域正向变化的面积大于负向变化,乡镇区域则相反,并且随着城市化进程的深入,各土地用地类型的植被覆盖度变化在一定程度上都呈现出生态退化的趋势。     

盐城海滨湿地植被地上生物量遥感估算研究

以盐城湿地自然保护区核心区的ETM+图像数据和同期野外实测的31 个样方地上生物量干重、湿重数据为数据源,分析了15 个遥感信息变量与湿地植被地上生物量干重、湿重的相关关系,并选择在0.01 水平上显著相关的8 个遥感变量建立一元线性回归模型、一元曲线回归模型以及多元逐步回归模型,并对比得出最优模型,进而计算出整个研究区的地上生物量。研究得出：①与研究区湿地植被地上生物量干重和湿重相关性最大的都是ETM+4 波段,干重的相关系数为0.833,湿重的相关系数为0.796;②研究区植被地上生物量干重和湿重的遥感估算模型都是一元三次函数模型,且干重模型的拟合精度要优于湿重模型;③得到研究区地上生物量干重总重量为2.28×10⁸ kg,湿重总重量为6.10×10⁸ kg。     

Landsat ETM+ Images in the Estimation of Seasonal Lake Water Quality in Boreal River Basins

We investigated the use of Landsat ETM+ images in the monitoring of turbidity, colored dissolved organic matter (CDOM), and Secchi disk transparency (Z(SD)) in lakes of two river basins located in southern Finland. The ETM+ images were acquired in May, June, and September 2002 and were corrected for atmospheric disturbance using the simplified method of atmospheric correction (SMAC) model. The in situ measurements consisted of water sampling in the largest lake of the region, routine monitoring results for the whole study area, and Z(SD) observations made by volunteers. The ranges of the water quality variables in the dataset were as follows: turbidity, 0.6-25 FNU; absorption coefficient of CDOM at 400 nm, 1.0-12.2 m(-1); Z(SD), 0.5-5.5 m; and chlorophyll a concentration, 2.4-80 mug L(-1). The estimation accuracies of the image-specific empirical algorithms expressed as relative errors were 23.0% for turbidity, 17.4% for CDOM, and 21.1% for Z(SD). If concurrent in situ measurements had not been used for algorithm training, the average error would have been about 37%. The atmospheric correction improved the estimation accuracy only slightly compared with the use of top-of-atmospheric reflectances. The accuracy of the water quality estimates without concurrent in situ measurements could have been improved if in-image atmospheric parameters had been available. The underwater reflectance simulations of the ETM+ channel wavelengths using water quality typical for Finnish lakes (data from 1113 lakes) indicated that region-specific algorithms may be needed in other parts of the country, particularly in the case of Z(SD). Despite the limitations in the spectral and radiometric resolutions, ETM+ imagery can be an effective aid, particularly in the monitoring and management of small lakes (<1 km(2)), which are often not included in routine monitoring programs.     

A Comparison of Spatial and Spectral Image Resolution for Mapping Invasive Plants in Coastal California

We explored the potential of detecting three target invasive species: iceplant (Carpobrotus edulis), jubata grass (Cortaderia jubata), and blue gum (Eucalyptus globulus) at Vandenberg Air Force Base, California. We compared the accuracy of mapping six communities (intact coastal scrub, iceplant invaded coastal scrub, iceplant invaded chaparral, jubata grass invaded chaparral, blue gum invaded chaparral, and intact chaparral) using four images with different combinations of spatial and spectral resolution: hyperspectral AVIRIS imagery (174 wavebands, 4 m spatial resolution), spatially degraded AVIRIS (174 bands, 30 m), spectrally degraded AVIRIS (6 bands, 4 m), and both spatially and spectrally degraded AVIRIS (6 bands, 30 m, i.e., simulated Landsat ETM data). Overall success rates for classifying the six classes was 75% (kappa 0.7) using full resolution AVIRIS, 58% (kappa 0.5) for the spatially degraded AVIRIS, 42% (kappa 0.3) for the spectrally degraded AVIRIS, and 37% (kappa 0.3) for the spatially and spectrally degraded AVIRIS. A true Landsat ETM image was also classified to illustrate that the results from the simulated ETM data were representative, which provided an accuracy of 50% (kappa 0.4). Mapping accuracies using different resolution images are evaluated in the context of community heterogeneity (species richness, diversity, and percent species cover). Findings illustrate that higher mapping accuracies are achieved with images possessing high spectral resolution, thus capturing information across the visible and reflected infrared solar spectrum. Understanding the tradeoffs in spectral and spatial resolution can assist land managers in deciding the most appropriate imagery with respect to target invasives and community characteristics.     

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSE_r = 39.88% for the training set, and RMSE_r = 42.36% for the testing set) than MLR (RMSE_r = 49.51% for the training, and RMSE_r = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m², respectively.     

基于Landsat／TM和ETM＋的理塘草地沙化趋势及其驱动力分析

文章利用1989、1994、2000和2005年的4期Landsat／TM，ETM＋遥感影像，在几何校正、辐射定标、大气校正的基础上计算了归一化植被指数（NDVI），建立最小二乘法线性回归方程对多时相NDVI进行同化处理，基于二分模型提取植被覆盖度并转换成荒漠化指数（DI），对理塘县城附近约103．1km2范围的分类研究结果表明：（1）分为无沙化、轻度、中度、重度和严重沙化5级，其中2005年中度、重度和严重沙化面积分别为2．562km2、2．925km2和1．576km2；中度沙化比1989年减少9．69％，重度和严重沙化分别增多35．79％和161．8％；（2）2005年中度以上沙化面积比1989年增加1．470km2，增加了26．3％，年增加率为1．47％；（3）沙化面积呈前期小、中期大幅增加、后期增加减缓的总体趋势；（4）降水量和风速因子的变化趋势有利于缓解沙化，气温变化趋势没有反映出对沙化的促进作用。人类活动是造成沙化趋于恶化的主要因素；（5）沙化增加趋势与同类研究结果一致。